University of Tasmania
Browse

Human identification via unsupervised feature learning from UWB radar data

Download (564.78 kB)
conference contribution
posted on 2023-05-23, 14:43 authored by Yin, J, Son TranSon Tran, Zhang, Q
This paper presents an automated approach to automatically distinguish the identity of multiple residents in smart homes. Without using any intrusive video surveillance devices or wearable tags, we achieve the goal of human identification through properly processing and analyzing the received signals from the ultra-wideband (UWB) radar installed in indoor environments. Because the UWB signals are very noisy and unstable, we employ unsupervised feature learning techniques to automatically learn local, discriminative features that can incorporate intra-class variations of the same identity, and yet reflect differences in distinguishing different human identities. The learned features are then used to train an SVM classifier and recognize the identity of residents. We validate our proposed solution via extensive experiments using real data collected in real-life situations. Our findings show that feature learning based on K-means clustering, coupled with whitening and pooling, achieves the highest accuracy, when only limited training data is available. This shows that the proposed feature learning and classification framework combined with the UWB radar technology provides an effective solution to human identification in multi-residential smart homes.

History

Publication title

Lecture Notes in Computer Science, volume 10937 - Proceedings of the 2018 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018): Advances in Knowledge Discovery and Data Mining

Pagination

322-334

ISBN

978-3-319-93033-6

Department/School

School of Information and Communication Technology

Publisher

Springer

Place of publication

Cham, Switzerland

Event title

2018 Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018): Advances in Knowledge Discovery and Data Mining

Event Venue

Melbourne, Australia

Date of Event (Start Date)

2018-06-03

Date of Event (End Date)

2018-06-06

Rights statement

Copyright 2018 Springer

Repository Status

  • Open

Socio-economic Objectives

Health related to ageing

Usage metrics

    University Of Tasmania

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC